{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "data = np.loadtxt(open(\"./data/feature.csv\",\"rb\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-2.42156587e+01,  2.42942720e+00, -2.46636975e+00, ...,\n",
       "         6.86800336e-01,  1.69439402e+00, -2.34323022e+00],\n",
       "       [ 6.46320806e+00,  3.67511165e+01,  8.38255336e+00, ...,\n",
       "         4.12121252e+00,  2.44689740e+00, -4.28348478e+00],\n",
       "       [-7.99030162e+00,  2.40438257e+00, -1.10300641e+01, ...,\n",
       "         1.77534453e+00, -4.44194030e-01,  7.86665571e-01],\n",
       "       ...,\n",
       "       [ 8.61143331e+00,  7.70129866e+00,  7.95240226e+00, ...,\n",
       "        -2.74252456e+00,  1.07112531e+00, -6.31925661e-02],\n",
       "       [ 8.40862199e+01,  2.04187340e+01,  8.05410372e+00, ...,\n",
       "         7.27554259e-01,  3.51339470e+00, -1.79079914e+01],\n",
       "       [-1.39534562e+01,  6.64621821e+00, -5.23030367e+00, ...,\n",
       "         8.25329076e-01,  1.38230701e+00, -2.41942061e+00]])"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
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